| Accelerating three-dimensional phase-field simulations via deep learning approaches | |
Zhou, Xuewei1,2; Sun, Sheng4; Cai SL(蔡松林)6 ; Chen, Gongyu1; Wu, Honghui3,5; Xiong, Jie4; Zhu, Jiaming1,2,5
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| Corresponding Author | Wu, Honghui([email protected]) ; Xiong, Jie([email protected]) ; Zhu, Jiaming([email protected]) |
| Source Publication | JOURNAL OF MATERIALS SCIENCE
 |
| 2024-09-01 | |
| Volume | 59Issue:33Pages:15727-15737 |
| ISSN | 0022-2461 |
| Abstract | Phase-field modeling (PFM) is a powerful but computationally expensive technique for simulating three-dimensional (3D) microstructure evolutions. Very recently, integrating machine learning into phase-field simulations provides a promising way to reduce calculation time remarkably. In this study, we propose a deep learning model that combines a convolutional autoencoder with a deep operator network to predict 3D microstructure evolution by using 2D slices of the 3D system. It is found that the deep learning model can shorten the calculation time from 37 min to 3 s after the initial training, while skipping 5-time steps, and reduce the phase-field simulation time by 31% in entire calculation of the evolution process. Interestingly, this model achieves good accuracy in predicting 3D microstructures by utilizing only 2D information. This work demonstrates the efficiency of machine learning in accelerating phase-field simulations while maintaining high accuracy and promotes the application of PFM in fundamental studies. |
| DOI | 10.1007/s10853-024-10118-4 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:001297590500002 |
| WOS Keyword | ALLOYS ; MODEL ; CAHN ; RECRYSTALLIZATION ; APPROXIMATION ; NETWORK |
| WOS Research Area | Materials Science |
| WOS Subject | Materials Science, Multidisciplinary |
| Funding Project | National Natural Science Foundation of China[12372152] ; National Natural Science Foundation of China[52122408] ; National Natural Science Foundation of China[52071023] ; National Natural Science Foundation of China[12072179] ; Qilu Young Talent Program of Shandong University, Zhejiang Lab Open Research Project[K2022PE0AB05] ; Shandong Provincial Natural Science Foundation[ZR2023MA058] ; Guangdong Basic and Applied Basic Research Foundation[2023A1515011819] ; Guangdong Basic and Applied Basic Research Foundation[2024A1515012469] |
| Funding Organization | National Natural Science Foundation of China ; Qilu Young Talent Program of Shandong University, Zhejiang Lab Open Research Project ; Shandong Provincial Natural Science Foundation ; Guangdong Basic and Applied Basic Research Foundation |
| Classification | 二类 |
| Ranking | 3 |
| Contributor | Wu, Honghui ; Xiong, Jie ; Zhu, Jiaming |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/96385 |
| Collection | 非线性力学国家重点实验室 |
| Affiliation | 1.Shandong Univ, Sch Civil Engn, Jinan 250061, Peoples R China; 2.Shandong Univ, Shenzhen Res Inst, Shenzhen 518057, Peoples R China; 3.Univ Sci & Technol Beijing, Inst Carbon Neutral, Beijing Adv Innovat Ctr Mat Genome Engn, Beijing 100083, Peoples R China; 4.Shanghai Univ, Mat Genome Inst, Shanghai 200444, Peoples R China; 5.Liaoning Acad Mat, Inst Mat Intelligent Technol, Shenyang 110004, Peoples R China; 6.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China |
| Recommended Citation GB/T 7714 | Zhou, Xuewei,Sun, Sheng,Cai SL,et al. Accelerating three-dimensional phase-field simulations via deep learning approaches[J]. JOURNAL OF MATERIALS SCIENCE,2024,59,33,:15727-15737.Rp_Au:Wu, Honghui, Xiong, Jie, Zhu, Jiaming |
| APA | Zhou, Xuewei.,Sun, Sheng.,蔡松林.,Chen, Gongyu.,Wu, Honghui.,...&Zhu, Jiaming.(2024).Accelerating three-dimensional phase-field simulations via deep learning approaches.JOURNAL OF MATERIALS SCIENCE,59(33),15727-15737. |
| MLA | Zhou, Xuewei,et al."Accelerating three-dimensional phase-field simulations via deep learning approaches".JOURNAL OF MATERIALS SCIENCE 59.33(2024):15727-15737. |
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